Abstract: Lung cancer ranks among the top causes of cancer-related fatalities globally and is frequently detected at later stages when therapeutic options are scarce. Timely diagnosis is vital for enhancing the survival rates of patients. This paper examines the use of the Support Vector Machine (SVM) algorithm for lung cancer prediction. SVM is a supervised machine learning method known for its efficacy in classification tasks, particularly in high-dimensional contexts. In this research, the dataset is evaluated using various attributes, including age, gender, smoking background, and imaging results, to train the SVM model. The study highlights the promise of machine learning methods, particularly SVM, in aiding healthcare professionals with early detection and improving patient outcomes.
Keywords: Lung cancer prediction, SVM, Patient Dataset, model training, Early detection.
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